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Emotion Recognition by Machine Learning Algorithms using Psychophysiological Signals
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- Authors
- Eun-Hye Jang, Byoung-Jun Park, Sang-Hyeob Kim, Jin-Hun Sohn
- Issue Date
- 2012-03
- Citation
- International Journal of Engineering and Industries, v.3, no.1, pp.55-66
- ISSN
- 2093-5765
- Publisher
- 차세대융합기술연구원(AICIT)
- Language
- English
- Type
- Journal Article
- Abstract
- Recently, emotion recognition systems based on physiological signals have introduced in humancomputer interaction researches. The aim of this study is to classify seven emotions (happiness, sadness, anger, fear, disgust, surprise, and stress) by machine learning algorithms using physiological signals. 12 college students participated in this experiment over 10 times. Total 70 emotional stimuli (10 emotional stimuli per each emotion) had been tested their suitability and effectiveness prior to experiment. Physiological signals, i.e. EDA, ECG, PPG, and SKT were acquired and were analyzed. Physiological signals were obtained prior to the presentation of emotional stimuli and while emotional stimuli were presented to participants. 28 features were extracted the acquired signals and analyzed for 30 seconds from the baseline and the emotional states. For emotion recognition, the data which is subtracted baseline values from the emotional state applied to 5 machine learning algorithm, i.e. FLD, CART, SOMs, Naïve Bayes and SVM. The result showed that an accuracy of emotion classification by SVM was highest and lowest by FLD. This means that SVM is the best emotion recognition algorithm in this study. Our result can help emotion recognition studies lead to better chance to recognize not only basic emotion but also user 's various emotions, e.g., boredom, frustration, love, pain, etc., by using physiological signals. Also, it is able to be applied on many human-computer interaction devices for emotion detection.
- KSP Keywords
- Baseline values, College students, Emotion Detection, Emotion classification, Emotion recognition, Emotional states, Interaction devices, Machine Learning Algorithms, Physiological signals, Recognition algorithm, emotional stimuli